New Evidence of the Marginal Predictive Content of Small and Large Jumps

Abstract

In an important paper, Bollerslev, Li, and Zhao (2019) find that there is significant predictive content in signed total jump variation. In this paper, we further decompose jumps into small and large (signed) variation. Our primary purpose is to investigate the marginal predictive content of small versus large jump variation, when forecasting oneweek ahead cross-sectional equity returns. However, we also examine earnings announcements, in order to shed new light on the linkages between (small and large) jumps and news. One of our key findings is that sorting on signed small jump variation leads to greater value-weighted return differentials between stocks in our highest and lowest quintile portfolios (i.e., high-low spreads) than when either signed total jump or signed large jump variation is sorted on. Moreover, in a key case, the high-low spread is not significantly different from zero when signed large jump variation is sorted on. Indeed, including large jump variation can actually decrease predictive accuracy, in the sense that average returns and alphas for high-low portfolios are lower when total jump variation is utilized in our prediction experiments rather than small jump variation. These results suggest that there may be a threshold, beyond which “large” jump variation contains no marginal predictive ability, relative to that contained in small jump variation. Analysis of returns and alphas based on industry double-sorts indicates that the benefit of small signed jump variation investing is driven by stock selection within an industry, rather than industry bets. Investors prefer stocks with a high probability of large positive jump variation, but they also tend to overweight safer industries. Additionally, the fact that large and small (signed) jump variation have differing marginal predictive content is explained at least in part by our observation that in double-sorted portfolios, the content of signed large jump variation is negligible when controlling for either signed total jump variation or realized skewness. By contrast, signed small jump variation has unique information for predicting future returns, even when controlling for total jump variation or realized skewness. Further, we find that large jumps are closely associated with “big” news, as might be expected. In particular, large earning announcement surprises increase both the magnitude and occurrence of large jumps. While such news related information is embedded in large jump variation, the information is generally short-lived, and dissipates too quickly to provide marginal predictive content for subsequent weekly returns. Finally, we find that while large jump variation is closely associated with large earnings surprises (“big” news), small jumps tend to be more closely associated with idiosyncratic risks, and can be diversified away.